I have a binary supervised classification problem with about 62 features, by eye about 30 of them could have reasonable discriminating power. Depending on the situation I have between 12,000 and 2,000 samples ( I consider a number of cases but the features are the same for all ).I am using sklearn and the MLP does not have a dedicated feature selection tool like decision trees do. My question is what is the recommended way to preform feature selection here? I have read in the sklearn documentation that LDA should not be performed in a binary classification problem and PCA is under the unsupervised methods on the sklearn website.
Does anyone have any experience with this that could suggest a method?
Edit: Added number of samples